The Use of Intelligent Sensing Algorithm for Internet of Things and Hash Spatial Information Location Technology

Abstract

This exploration is aimed at quickly obtaining the spatial position information of microseismic focal points and increasing the accuracy of microseismic rapid positioning, to take timely corresponding measures. A microseismic focal point location system completely different from the traditional microseismic location method is proposed. The search engine technology is introduced into the system, which can locate the microseismic focal point quickly and accurately. First, the propagation characteristics of microseismic signals in coal and rock layers are analyzed, and the focal position information is obtained. However, the collected microseismic signal of the coal mine contains noise, so it is denoised at first. Then, a waveform database is established for the denoised waveform data and focal point position. The structure and mathematical model of the location-sensitive hash (LSH) based on $P$ stable distribution are introduced and improved, and the optimized algorithm multiprobe LSH is obtained. The microseismic location model is established according to the characteristics of microseismic data. The values of three parameters, hash table number, hash function family dimension, and interval size, are determined. The experimental data of the parameters of the search engine algorithm are analyzed. The results show that when the number of hash tables is 6, the dimension $k$ of the hash function family is 14, and the interval size $W$ is 8000, the retrieval time reaches a relatively small value, the recall rate reaches a large value, and the proportion of retrieved candidates is large; the parameters of the search engine algorithm of the measured coal mine microseismic data are analyzed. It is obtained that when the number of hash tables is 4, the dimension $k$ of the hash function family is 6, and the interval size $W$ is 500, the retrieval time reaches a relatively small value, the recall rate obtains a large value, and the proportion of retrieved candidates is large. The contents studied are of great significance to the evaluation of destructive mine earthquakes and impact risk.